Anomaly detection and protection

ABSTRACT

An apparatus for detecting an anomaly in an electronic system embodying at least two integrated circuits, and where necessary, removing/mitigating the anomaly. The anomaly detection is based on sensing the characteristics of either the current, the voltage, or both the current and voltage of the supply rail connected to the at least two integrated circuits. When an anomaly occurs, the anomaly is detected by one sensing circuit sensing that the characteristics are different from that when the electronic system is functioning normally.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority of Singapore patentapplication No. 10202202290T, filed 8 Mar. 2022, the content of it beinghereby incorporated by reference in its entirety for all purposes.

TECHNICAL FIELD

Various embodiments relate to electronic designs to detect an anomaly inan electronic system, and where possible remove or mitigate the anomaly.The anomaly may be due to radiation effects, malfunction, computationalerrors, etc.

BACKGROUND

In some applications, including on-earth and space/satelliteapplications, the reliability of electronic systems embodying (includingintegrated circuits (ICs), System-on-Chip (SoC), System-in-Package(SiP), etc.; henceforth collectively termed ICs), is one of the mostimportant design considerations. To enhance the reliability of theelectronic system, it is useful to detect the occurrence of an anomalyand if possible, provide means to remove or mitigate the anomaly. Theanormal may be due to radiation effects include single-eventeffects—single-event-latchup (SEL), single-event-transient andsingle-event-upset—or total-ionization dosage, enhanced low dose rateeffects, neutron and proton displacement damage. It may also be due tonon-radiation effect failures such as malfunction, computation errors,etc.

In the case of an SEL anomaly, a short-circuit may be induced within theIC and this may render non-functionality of and damaging the IC. Therange of the SEL current (the short-circuit current) is wide. When it ishigh, e.g., 5× the operating current, it is easily detected by means ofa current sensor whose threshold (e.g., 4×) is lower than the SELcurrent. When the threshold is exceeded, i.e., an anomaly is detected,and power-cycling (disconnecting and reconnecting the specific powerrail (or some or all of the power rails) to the IC) is activated toremove the SEL.

In addition to SELs, there are micro-SELs where their short-circuitcurrent may be very low—possibly much lower than the operating currentof the IC. In some ICs, such as a complex Field Programmable Gate Array(FPGA), the micro-SEL (and SEL) current is variable. Because of its lowcurrent and variability, it is difficult to detect. Nevertheless, it isimportant to detect and remove both SEL and micro-SEL in an IC becausethey compromise reliability.

For completeness, a complex integrated circuit, such as an SoC (e.g., anFPGA) may be considered an electronic system and considered as embodyingseveral ICs and/or several electronic devices.

Such anomalies may also occur in due to other faults such asmalfunction, computation errors, etc., and the range of the ensuingcurrent drawn by the IC with anomality is also large. Such anomalies mayalso be difficult to detect.

Hitherto, the detection of SELs and micro-SELs are limited to monitoringthe current in one power rail [1] connected to one pin of an IC. Thereis also some nascent effort to employ machine-learning (ML) to detectSELs and micro-SELs. Nevertheless, the Machine Learning (ML) efforts [2]are similarly limited to monitoring the current in one power rail andthe algorithm is simplistic, rendering limited detection accuracy.

SUMMARY

In an embodiment, an apparatus for detecting an anomaly in an electronicsystem is disclosed. The apparatus comprises a power rail connectedbetween a power source and a supply rail, the supply rail connected to adistributed power rail, and the distributed power rail connected to afirst sub-supply rail and to a second sub-supply rail. The apparatusalso comprises a first electronic device and a second electronic device.The first sub-supply rail is connected to a first pin of the firstelectronic device or a first pin of the second electronic device. Thesecond sub-supply rail is connected to a second pin of the firstelectronic device or to a second pin of the second electronic device.The apparatus additionally comprises a signal processing system thatincludes a sensing circuit. The sensing circuit senses thecharacteristics of the current, the voltage, or both the current andvoltage of the power rail or the supply rail and detects the anomalywhen the anomaly occurs in either the first electronic device or thesecond electronic device or both electronic devices

In another embodiment, a method for detecting an anomaly in anelectronic device is disclosed. The method comprises sensing, by asensing circuit of a signal processing system in the electronic system,characteristics of the current, the voltage, or both the current orvoltage of a power rail of the electronic system or a supply rail of theelectronic system. The power rail is connected a power source and to thesupply rail, The supply rail is connected to a distributed power rail.The distributed power rail is connected to a first sub-supply rail andto a second sub-supply rail. The first sub-supply rail is connected to afirst pin of a first electronic device or to a first pin of a secondelectronic device or to the first pin of both the first and the secondelectronic devices. The second sub-supply rail is connected to a secondpin of the first electronic device or to a second pin of the secondelectronic device or to the second pin of both the first and the secondelectronic devices. The method also comprises when the anomaly occurs ineither the first electronic device or the second electronic device orboth the electronic devices, detecting the anomaly by the sensingcircuit sensing that the characteristics are different from that whenthe electronic system is functioning normally.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, like reference characters generally refer to like partsthroughout the different views. The drawings are not necessarily toscale, emphasis instead generally being placed upon illustrating theprinciples of the invention. In the following description, variousembodiments of the invention are described with reference to thefollowing drawings, in which:

FIG. 1 is a first example of an electronic system having an anomalydetection methodology by sensing the supply rail according to theembodiment, to detect/qualify an anomaly occurring within at least oneelectronic device in a COTS system.

FIG. 2(A) is the onboard power management having a connector—a passiveonboard power management comprising a short-circuit—within the COTSsystem.

FIG. 2(B) is the onboard power management having a DC-DC converterwithin the COTS system.

FIG. 3 is a block diagram of the anomaly detection methodology in FIG. 1according to an embodiment of the invention.

FIG. 4 is a block diagram of the anomaly detection methodology in FIG. 3according to an embodiment of the invention.

FIG. 5(A) is an example of anomaly signature—an SEL current profileextracted from an FPGA.

FIG. 5(B) is an example of a modelled Anomaly signature which may beinfluenced by other conditions such as temperature variation, loadingconditions attributed by I/O circuits (and/or other circuits) and theimpact due to power-cycling and recovery (e.g., re-start conditions).

FIG. 6 is a Profiling Online Anomaly Signature block in FIG. 4 accordingto an embodiment of the invention.

FIG. 7 is an example by ranking seven profiled parameters for thedetection of an anomaly.

FIG. 8 is an Anomaly Detection block in FIG. 4 according to anembodiment of the invention.

FIG. 9 is a second example of an electronic system having an anomalydetection methodology by sensing two supply rails according to anembodiment of the invention, to detect/qualify an anomaly occurringwithin at least one electronic device in a COTS system.

FIG. 10 is a third example of an electronic system having an anomalydetection methodology by sensing the supply rail and/or monitoring theoutput of a COTS system according to an embodiment of the invention, todetect/qualify an anomaly occurring within at least one electronicdevice in the COTS system.

DETAILED DESCRIPTION

It should be understood at the outset that although illustrativeimplementations of one or more embodiments are illustrated below, thedisclosed systems and methods may be implemented using any number oftechniques, whether currently known or not yet in existence. Thedisclosure should in no way be limited to the illustrativeimplementations, drawings, and techniques illustrated below, but may bemodified within the scope of the appended claims along with their fullscope of equivalents.

The description herein refers to the accompanying drawings that show, byway of illustration, specific details and embodiments in which theinvention may be applied. These embodiments are delineated in detail toenable the skilled in the art to supply the invention.

As used herein, the term “and/or” includes any and all combinations ofone or more of the associated listed items.

According to an aspect of the present disclosure, there is provided anapparatus for detecting an anomaly in an electronic system in partembodying a COTS system having at least two ICs (which may be in onecomplex IC, e.g., FPGA), where necessary for removing/mitigating theanomaly. The anomaly may be due to radiation effects or non-radiationeffect failures such as malfunction and computation errors. The anomalydetection is based on the sensing the characteristics of either thecurrent, the voltage, or both the current and voltage electricallycoupled to the COTS system, and when an anomaly occurs, the anomaly isdetected by one sensing circuit sensing that the characteristics aredifferent from that when the electronic system is functioning normally(i.e., without an anomaly).

In some embodiments, the anomaly detection methodology involves theemployment of a processing unit (or signal processing circuit) havingpre-characterized anomaly characteristics of one or both ICs or anyother IC or any power management circuit in part embodied in the COTSsystem. When the anomaly occurs, the processing unit correlates thepre-characterized anomaly characteristics of one or both ICs or anyother IC or any power management circuit with the sensed characteristicsby the sensing circuit.

In some embodiments, the anomaly detection methodology causes the COTSsystem to be temporarily or permanently electrically decoupled from thepower source or to be electrically discharged to a lower voltage, sothat anomaly in the electronic system can be removed/mitigated.

In some embodiments, the anomaly detection methodology involves the useof an analog processing unit, or a digital processing unit or amixed-signal processing unit.

In some embodiments, the anomaly detection methodology involves havingtwo sensing circuits sensing the characteristics of two currents,voltages, or both the currents and voltages electrically coupled to theCOTS system. When an anomaly occurs, the anomaly is detected by at leastone of the two sensing circuits sensing that the characteristics aredifferent from that when the electronic system is functioning normally.

In some embodiments, the anomaly detection methodology is to have onesensing circuit sensing the characteristics of the current, the voltage,or both the current or voltage electrically coupled to the COTS systemand to have one output signal monitoring the operational conditionstatus on the COTS system. When an anomaly occurs, the anomaly isdetected by the sensing circuit sensing that the characteristics aredifferent from that when the electronic system is functioning normally.It is also detected by monitoring the output signal which is anindication of the operational condition of the COTS system.

In some embodiments, the anomaly detection methodology involves theemployment of statistical parameters of the characteristics of thesignal sensed by the sensing circuit for its detection of an anomaly,and where necessary to facilitate the anomaly detection by machinelearning algorithms by means of labelling, training and weighting thestatistical parameters.

The system having the anomaly detection methodology according to someembodiments of the present disclosure is different from and improves theanomaly detection over the existing systems. For example, a prior-art isan over-current protection by merely sensing a load current of thesystem and then comparing the load current against a pre-determinedthreshold. The system according to some embodiments of the presentdisclosure is beyond an over-current protection. Another prior-art is achip-level latch-up current protection [1], [2] to protect only onesingle IC—not in a system—by sensing a latch-up current directly flowinginto the IC, and thereafter detecting the latch-up event (if any) fromthe latch-up current. Further, there is no correlation between any ICsas the protection is for only one IC. The system according to someembodiments of the present disclosure is a system-level protection toprotect at least two ICs, by embodying signal processing intelligence tocorrelate the pre-characterized anomaly characteristics of one or bothICs or any other IC or any power management circuit with the sensedcharacteristics by the sensing circuit. The correlation may beestablished by processing a correlation threshold value which may be abinary value, a probability value, or a relative change of normalizedvalue. The correlation threshold value is processed by involving anysensed characteristics by one or more sensing circuits in thepower/supply rails, and/or any feedback output (operational status) fromthe system.

Turning to FIG. 1 , FIG. 1 depicts Power Supply 100 providing current(and/or voltage) to the invented System Latchup Detection and Protection(SLDAP) Circuit 140 and COTS (Commercially-Off-The-Shelf) System 120.The COTS System 120 may be commercially available, e.g., a GPS system,or it may be propriety, i.e., not commercially available. Although theinvented SLDAP Circuit 140 is depicted here as a separate entity to theCOTS System 120, it may be incorporated into the COTS System 120. Forsake of readability, the invented SLDAP Circuit 140 is describedhenceforth as a separate entity. The Electronics System 190 comprisesour invented SLDAP Circuit 140 and the COTS System 120.

The Power Supply 100 and the COTS System 120 may be electrically coupledvia a Power Rail 102, a Supply Rail 104, and a Switch 106. The PowerSupply 100, via the Power Rail 102, the Switch 106, and the Supply Rail104, may supply a combined current (and/or voltage) to an Onboard PowerManagement 122 in the COTS system 120. The Onboard Power Management 122,via the Distributed Power Rail 124, may further distribute current(and/or voltage) to power a number of COTS ICs, e.g., from the firstCOTS IC (COTS-1 130) to the n-th COTS IC (COTS-n 132).

Note that the COTS ICs may be that commercially available or it may bepropriety. Further, as delineated in the “Background” section, a complexintegrated circuit, such as an SoC (e.g., an FPGA) may be considered anelectronic system and considered as embodying two or more ICs and/or twoor more electronic devices. For example, COTS-1 130 and COTS-n 132 maybe the same integrated circuit, e.g., two different parts or twoelectronic devices of an FPGA.

The Distributed Power Rail 124 may form a single or collective set oftwo or more Sub-power rails 126 to 128 to supply current (and/orvoltage) to the various COTS ICs (or different parts of an integratedcircuit). For example, the Sub-power Rail 126 may supply current (and/orvoltage) to power COTS-1 130, the Sub-power Rail 128 may supply current(and/or voltage) to power COTS-n 132, and other sub-power rails (notshown) may supply current (and/or voltage) to other COTS ICs (notshown). The voltages of the Sub-power rails 126 and 128 and othersub-power rails may or may not be the same. The COTS System 120 mayconstitute varied configurations such as that comprising at least oneintegrated circuit (e.g., an FPGA) or COTS IC, an evaluation board of aCOTS IC, a complex electronics system with a multiplicity of COTS ICs ona printed circuit board, and it may be that commercially available,proprietary, etc.

The Power Supply 100 may be a battery or from another power managementsystem comprising a DC-DC converter(s), a low dropout (LDO) regulator,etc.

The combined current (and/or voltage) to the Onboard Power Management122 in the COTS system 120 may need to be sensed/monitored for detectionof an anomaly, e.g., an SEL or a micro-SEL. The sensing of the combinedcurrent (and/or voltage) may be placed on the Power Rail 102 or theSupply Rail 104. For illustration, in FIG. 1 , the sensing of thecombined current (and/or voltage) may take place on the Supply Rail 104where the current in (and/or the voltage) of the Supply Rail 104 may besensed/monitored as indicated by the Sensing Circuit 142 to the inventedSystem Latchup Detection and Protection (SLDAP) Circuit 140; note thatthe Sensing Circuit 142 may be incorporated into the invented SLDAPCircuit 140.

By means of the Sensing Circuit 142, the SLDAP Circuit 140 may performthe necessary tasks to ascertain (i.e., detect or recognize) if ananomaly has occurred in one or more of the COTS ICs in the COTS System120, e.g., one or more in the n COTS in FIG. 1 , from COTS-1 130 toCOTS-n 132. When an anomaly is detected, the SLDAP Circuit 140 maytrigger the Control Line 144 to power cycle the Switch 106. Conversely,where an anomaly is not detected, the Control Line 144 may keep theSwitch 106 closed, thereby electrically coupling the Power Rail 102 andthe Supply Rail 104. The power cycling may involve reducing the voltageof the supply rail or the supply to a pre-determined voltage at leasttemporarily.

If the sensing of the combined current (and/or voltage) takes place onthe Power Rail 102, the Sensing Circuit 142 may be re-located forsensing the Power Rail 102. By means of the Sensing Circuit 142associated with the Power Rail 102, the SLDAP Circuit 140 and the Switch106 may perform the same anomaly detection operation as delineatedearlier.

For simplicity, the combined current (and/or voltage) to the COTS system120 is termed as the combined sensing parameter where the parameter maybe voltage or current or both current and voltage. Note that for currentsensing, the Sensing Circuit 142 may include the insertion of anelectronic element, e.g., a small resistor, within the Supply Rail 104where the voltage across the said electronic element is an indication ofthe current flowing through it.

Collectively, the Switch 106, the Sensing Circuit 142, and the SLDAPCircuit 140 collectively form the embodiment of the invention termed asa SLDAP Apparatus 180. Further, the SLDAP Circuit 180 and the COTSSystems 120 constitute the Electronics System 190.

Consider the SLDAP Apparatus 180 where the intention is the detection ofan anomaly in the COTS System 120, i.e., in at least one of thepins/pads (e.g., a VDD voltage input or an I/O) of at least one COTS ICthat suffers from an anomaly. For example, consider the case where theSub-power Rail 126 supplies current into (and/or voltage at) Pin 156 ofthe COTS-1 130, and the Sub-power Rail 128 supplies current into (and/orvoltage at) Pin 158 of the COTS-n 132. When an anomaly occurs in a pin,the current in (and/or voltage at) that pin may experience unexpectedinstantaneous (and/or over a period of time) transients—unexpected inthe sense that such transients do not occur in usual non-anomalous ornominal (usual) operation.

This anomaly current (and/or voltage) has the characteristics of ananomaly and is henceforth the basis of the anomaly ‘signature’ of thatpin. For sake of clarity, note that “characteristics of an anomaly” and“anomaly signature” are henceforth used interchangeably. The anomalysignature may include any spatial, temporal, chronological signals andderivations associated with the transients, and signal processingperspectives such as in the frequency domain and other transformations,direct, indirect, linear, non-linear, iterative, recursive, by means ofmachine learning (artificial intelligence), etc. —discussed later. Forexample, when an SEL occurs in Pin 156 connected to Sub-power Rail 126,the unexpected instantaneous transient current 160 (and/or voltage) maybe the basis of the SEL (anomaly) signature of Pin 156 of the COTS-1130.

At the outset, note that it is desirable (but not necessary) that allanomaly signatures of a given COTS IC and of all COTS ICs 130 to 132 inCOTS System 120 are obtained so as to facilitate the detection of everypossible anomaly. The anomaly signature of a specific pin (e.g., the3.3V VDD input supply pin or the I/O pad) of a COTS IC may be obtainedfrom measurements both in terms of the current into and the voltage atthe pin of interest. This anomaly signature also in the voltage domainmay be useful as the voltage often overshoots and/or undershoots whenthere are changes to the current, including during an anomaly. Theanomaly signature may further include the current (and/or voltage)transients and/or waveform preceding the occurrence of an anomaly,instantaneous current (and/or voltage) at the juncture of the occurrenceof an anomaly, and following the instance of the occurrence of ananomaly, i.e., a time frame comprising a series of current (and/orvoltage) samples. The anomaly signature may further includefrequency-domain transformations using the current (and/or voltage)transients from the time-domain, etc.

If a specific anomaly signature of a pin of a COTS IC cannot be obtainedfrom measurements, a ‘pseudo’ anomaly signature such as the profile ofan irregular current surge (and/or voltage transients) may be assumed tobe the basis of an anomaly signature; irregular may be interpreted asunexpected, i.e., not resembling that in usual (i.e., non-anomalous)nominal ‘error-free’ operation or beyond what is expected or known to bewhen that COTS IC is operating normally. Further, the specific anomalysignature may also be modeled, or assumed, or estimated, or obtainedfrom data science such as machine learning, etc. The term ‘anomalysignature’ henceforth would include that measured, modeled, assumed,estimated, or obtained from data science such as machine learning, etc.

Note that a typical COTS IC has several supply rail pads/pins and I/Opads/pins (e.g., same or different VDD and of the same or differentvoltages, analog VDD, digital VDD, etc.). An anomaly may occur in morethan one supply rail or distributed power rails connected to one of theSub-power Rails 126 to 128. In the following delineations, when aparticular sub-power rail is referred to, it is assumed that there is apossibility that the referred sub-power rail may include more than onesub-power pin or pad of the COTS IC(s), and the various sub-power railsmay be of the same or different voltages. Also, as delineated earlier, acomplex integrated circuit, such as an SoC (e.g., an FPGA) may beconsidered an electronic system and considered as embodying two or moreICs and/or two or more electronic devices.

The basis of the anomaly signature is the current in (and/or thevoltage) of Sub-supply Rail 126 when COTS-1 130 suffers from an anomaly.When an anomaly occurs, the anomaly signature in Sub-supply Rail 126would also appear in Distributed Power Rail 124, possibly identically,or related to, or correlated to, or resembled in some relatedform—termed the ‘Resembled Anomaly Signature’. For example, theunexpected Instantaneous Transient Current 162 (and/or voltage) may bethe Resembled Anomaly Signature in Distributed Power Rail 124 having insome related form (or in some fashion, having some correlation) to theunexpected Instantaneous Transient Current 160 (and/or voltage) as theAnomaly Signature in the Sub-Supply Rail 126. The current in and/orvoltage of the Distributed Power Rail 124 would further comprise thetransients (and steady-state) of the operating currents in (and/orvoltage) of the other COTS IC, including COTS-2 (not shown) to COTS-n132.

In the Supply Rail 104, a ‘Further-Resembled Anomaly signature’ would bepresent—this may be identical, related, correlated to, or resembled insome form to the said Resembled Anomaly signature in Distributed PowerRail 124 and/or to the Anomaly signature in Sub-power Rail 126. Forexample, the unexpected instantaneous transient current 164 (and/orvoltage) may be the basis of the Further-Resembled Anomaly signature.The transients (and steady-state) of the operating voltage and currentin the Supply Rail 104 may be identical, related or correlated orresembling in some form to the operating voltage and current to the saidCOTS ICs (including COTS-2 (not shown) to COTS-n 132) would similarly bepresent. Put simply, when an anomaly occurs in COTS-1 130 in COTS System120, the combined current in and voltage of the Supply Rail 104 wouldcomprise both the Further-Resembled Anomaly Signature in some form(discussed later) and the operating (transients and steady-state)currents (and/or voltages) of COTS-2 (not shown) to COTS-n 132 in theCOTS System 120.

Note that in response to an anomaly event on any of the COST ICs withinthe COTS System 120, the Anomaly signature, Resembled Anomaly signatureand Further-Resembled Anomaly signature may be considered real-time,happening at the same time or almost at the same time, usually <500 μs,but could be longer depending on the delay due to the circuits in theelectronics system, including the power management. For simplicity, anonline Anomaly signature is used, where online refers to theavailability of an anomaly signature and observable on a power rail, asupply rail, a distributed supply rail, or a sub-power rail. The onlineAnomaly signature may be a real-time Anomaly signature, a real-timeResembled Anomaly signature or a real-time Further-Resembled Anomalysignature.

The Anomaly signature, Resembled Anomaly signature and Further-ResembledAnomaly signature may be predetermined/pre-characterized—they may beobtained by measurements, modeled, assumed, estimated, or obtained fromdata science such as machine learning, etc.

In short, the SLDAP Apparatus 180 ascertains that an anomaly hasoccurred in the COTS System 120 on the basis of identifying that anonline Signature (e.g., the Further-Resembled Anomaly Signature 164 inFIG. 1 ) is present in the Supply Rail 104. This means that thetransients (instantaneous value or frames of values, transformations,etc., as delineated earlier) of the current in (and/or voltage) at theSupply Rail 104 is ‘sufficiently similar’ to a pre-characterized Anomalysignature; ‘sufficiently similar’ will be delineated in the signalprocessing section herein later. Put simply, the SLDAP Apparatus 180detects an anomaly in the COTS System 120 by recognizing in some formthe Further-Resembled Anomaly Signature (current and/or voltage) in theSupply Rail 104.

Consider now the case where the Onboard Power Management 122 is simply apassive connector (a short-circuit) in FIG. 2(A), and that an anomalyoccurs in the Sub-supply Rail 126. Because the Onboard Power Management122 is a short-circuit, the Further-Resembled Anomaly Signature in theSupply Rail 104 would largely be the same as the Resembled Anomalysignature in the Distributed Power Rail 124 and as the Anomaly signaturein the Sub-Power Rail 126.

Consider the case where the Onboard Power Management 122 is active(e.g., having a DC-DC Converter) in FIG. 2(B), and that an anomalyoccurs in the Sub-supply Rail 126. The Further-Resembled AnomalySignature in the Supply Rail 104 will likely be different butnevertheless having some resemblance or related or correlated to in someform to the Resembled Anomaly signature in the Distributed Power Rail124 and to the anomaly in the Sub-supply Rail 126.

The Onboard Power Management 122 may have other configurations, e.g.,having multiple DC-DC Converters (or circuits) powered by the SupplyRail 104 to provide more than one distributed power rails or sub-powerrails, or having multiple DC-DC circuits to provide one or moredistributed power rails or sub-supply rails. Despite these variedvarious configurations, the Further-Resembled Anomaly signature in theSupply Rail 104 will likely be different but nevertheless having someresemblance or related or correlated in some form to the ResembledAnomaly Signature in the distributed power rail(s) and to the AnomalySignature in the sub-power rail(s).

For completeness, note that an anomaly may also occur in the OnboardPower Management 122. If the anomaly occurs in the Onboard PowerManagement 122, the detection mechanism(s) by the SLDAP Apparatus 180delineated above would likewise apply.

FIG. 3 depicts the building blocks of the SLDAP Apparatus 180, havingthe Switch 106, the Sensing Circuit 142 having a sensor (e.g., resistor)R_(sense) 302, the SLDAP Circuit 140 having a Current Conversion Circuit304 and a Signal Processing Circuit 306. Note that the CurrentConversion Circuit 304 may also be a voltage conversion circuit or botha current and voltage conversion circuit. For sake of brevity, thefollowing delineation is largely on current—note that the signal ofinterest may also be voltage or a combination of current and voltage.

The sensor R_(sense) 302 is used to sense the combined current flowinginto the COTS System 120. The sensing may be achieved by measuring thevoltage (V) across R_(sense) and dividing V by R_(sense). The sensingoperation may be achieved by a comparator embodied in the SensingCircuit 142. The Current Conversion Circuit 304 processes the combinedcurrent into the processed combined current to allow the SignalProcessing Circuit 306 to identify, detect or qualify if an anomaly hasoccurred in any of the COTS ICs in the COTS System 120. The processedcombined current obtained from the combined current may be in digitalform or analog/mixed-signal form. The Signal Processing Circuit 306 mayprocess the processed combined current in a digital modality or in ananalog/mixed-signal modality or in a combined digital andanalog/mixed-signal modality.

For illustration based on the digital modality, FIG. 4 depicts thebuilding blocks with the SLDAP Circuit 140, having the CurrentConversion Circuit 304 which is an analog-to-digital Circuit, and theSignal Processing Circuit 306 which comprises a pre-characterizedAnomaly Signature block 410, a profiling online Anomaly Signature block412, and an Anomaly Detection block 414. The pre-characterized AnomalySignature block 410 stores the pre-characterized anomaly signatures. Theprofiling online Anomaly Signature block 410 profiles the processedcombined current into useful online anomaly signatures so that theAnomaly Detection block 414 can correlate the similarity between thepre-characterized anomaly signatures and the online anomaly signature.If the similarity between the pre-characterized anomaly signatures andthe online anomaly signature is high, i.e., the observability of theanomaly occurrence in the COTS system 120 can be observed, the SLDAPCircuit 140 can be configured to trigger the Control Line 144 to powercycle the Switch 106, enabling the power-cycling to the COTS system 120.

FIG. 5(A) depicts an example of anomaly signature 502 (e.g., of an SEL)extracted from an FPGA by means of laser testing. The anomaly signaturemay be extracted from other ICs, e.g., microcontrollers, mixed-signalchips, or customized/propriety ICs which may experience anomalies suchas SEL under irradiation.

Note that the IC used for pre-characterization may or may not be thesame as the IC embodied in the COTS System 120. For improved detectionof an anomaly, it is desirable that the database of thepre-characterized anomaly signatures be large, including as manydifferent conditions as possible, and of any other device (i.e., notnecessarily the same devices in the COTS System 120 in FIG. 1 ). Forexample, in the case of radiation effects, it would be useful for thedatabase to embody anomaly signatures from various irradiations,including heavy-ion, proton, neutron, photon, X-ray, electron beams,etc. FIG. 5(B) depicts an example of modelled Anomaly signature 504which may be influenced by other conditions such as temperaturevariation, loading conditions attributed by I/O circuits (and/or othercircuits) and the impact due to power-cycling and recovery (e.g.,re-start conditions). The SEL signatures 502 and 504 are based on acurrent-time profile. The Anomaly signatures 502 and 504 may betransformed into other profiles, including in frequency domain.

FIG. 6 depicts the Profiling Online Anomaly Signature block 412. Theprofiling process may leverage on the statistic parameters based on theprocessed combined current—this profiling is termed as “Profiling byStatistic Parameters” 602. The parameters used may be maximum value,minimum value, mean, variance, standard variation, skewness, kurtosis,median-absolute deviation (MAD), k-statistic, and any combination ofthese statistical parameters. These statistical parameters arewell-established in literature.

The profiling process may leverage on the rate of charge based on theprocessed combined current—this profiling is termed as “Profiling byRate-of-Change” 604. The rate-of-change may be defined as R=unit changefor function 1 divided by unit change for function 2. The function 1 isrelated to the processed combined current and the function 2 is relatedto the time unit. For example, a possible first rate-of-change may bedefined as R₁=ΔI/Δt where I is the processed combined current, t is thetime, ΔI is the change in the combined current, and Δt is the unitchange in time. A possible second rate-of-change be defined as R₂=ΔI_(i)²/Δt where I_(i) ² is the square of the processed combined current, t isthe time, ΔI_(i) ² is the change in the square of the processed combinedcurrent, and Δt is the unit change in time. A possible third rate ofchange merit may be defined as R₃=ΔI_(i) ²/Δt² where I_(i) ² is thesquare of the processed combined current, t² is the square of time,ΔI_(i) ² is the change in the square of the processed combined current,and Δt² is the change in the square of time. Various other ways may bepossible, by performing a mathematical transformation (e.g., addition,subtraction, multiplication, and division) on the function 1 andfunction 2.

The profiled parameters either from Profiling by Statistic Parameters602 or Profiling by “Rate-of-Change” 604 may be further managed byFeature Extraction Management 604. The Feature Extraction Management 606may prioritize which profiled parameters (or features) having thepossible highest impact for detection of an anomaly. FIG. 7 depicts anexample by ranking seven profiled parameters, including MAD 702,Standard Derivation (STD) 704, Mean 706, Maximum Value (MAX) 708,K-statistic 710, Variance (VAR) 712, and Minimum Value (Min) for thedetection of an anomaly, e.g., SEL. For this example, the evaluationindicates MAD 702 having the highest impact, and Min 714 having thelowest impact. Having said that, a conservative approach may include allthese parameters collectively for detection of an anomaly (at the costof higher processing needs), and a customized approach may include thetop four parameters for the detection of an SEL.

FIG. 8 depicts the Anomaly Detection block 414 having an AnomalyDetection Modelling sub-block 802 and an Anomaly Threshold Sub-block804. The Anomaly Detection Modelling sub-block 802 collectively analyzesthe pre-characterized Anomaly Signatures and profiled online AnomalySignatures, aiming to extract the salient features related to theAnomaly. The modelling algorithms may involve machine learningalgorithms, by means of labelling, training and weighting the onlineAnomaly Signatures. The machine-learning detection algorithms mayinclude decision tree, random forest, naïve bayes, support vectormachine, K-nearest neighbors (KNN) or convolution neural network (CNN).The Anomaly Threshold Activation sub-block 804 may determine the anomalythreshold value, Anomaly Threshold 806, to be deemed as a probableanomaly observed by the SLDAP Apparatus 180 while monitoring the COTSsystem 120 for anomalies.

The value of Anomaly Threshold 806 may depend on the modellingalgorithms and/or the profiled parameters. For example, using the randomforest algorithm on the statistical parameters, the anomaly detectiondecision is binary, e.g., Anomaly Threshold 806 is asserted to ‘1’ (or‘0’) for a probable anomaly event in the COTS system 120 or otherwise to‘0’ (or ‘1’) for no-anomaly event observed in the COTS system 120. Foranother example, using the KNN algorithm on the statistical parameters,the anomaly detection decision is probability, e.g., Anomaly Threshold806 is calculated to be >50% for a probable anomaly event in the COTSsystem 120 or otherwise to be <50% for no-anomaly event observed in theCOTS system 120. For yet another example, using the correlationalgorithm based on the rate-of-change parameters, the Anomaly Threshold806 is the correlation coefficient which will be considered as aprobable anomaly in the COTS system 120 if the correlation coefficienthas >2 times change in magnitude between the non-anomaly operation vsanomaly operation. Otherwise, the no-anomaly event is considered.

For clarity, the above-mentioned anomaly detection decision is based ona correlation by establishing the similarity relationship between thepre-characterized Anomaly Signatures and profiled online AnomalySignatures. The correlation may be considered as true when it exceeds acorrelation threshold value. The correlation threshold value may be abinary value, a probability value, a relative change of normalizedvalue, an absolute value, and other combination of these values.

Once a probable anomaly is determined, the SLDAP Circuit 140 may triggerControl Line 144, and may adjust the time duration to how Control Line144 is to be activated.

FIG. 9 further depicts second embodiment of an Electronics System 990comprising invented SLDAP Apparatus 980 and COTS system 920, powered bytwo supply rails connected to two power supplies, Power Supply 100 andPower Supply 900. Note that although Power Supply 100 and Power Supply900 are depicted as two entities, they can be one same entity. Thevoltage of Power Supply 100 and Power Supply 900 may be the same or theybe different. For ease of comparison with FIG. 1 , the various entitlesin FIG. 1 are now referred to with a preceding ‘First’ in FIG. 9 , wherepertinent.

In this embodiment of the invention, the SLDAP Apparatus 980, has theFirst Power Rail 102, the First Supply Rail 104, the Second Power Rail902, and Second Supply Rail 904. The First Power Rail 102 and SecondPower Rail 902 may be connected to the Power Supply 100 and Power Supply900 respectively. The First Supply Rail 104 which may be electricallycoupled to the First Power Rail 102 via the Switch 106, and the SecondSupply Rail 904 may be electrically coupled to the Second Power Rail 902via the Switch 906. Both First and Second Supply Rails 104 and 904 areconnected to the Onboard Management 922 in the COTS system 920.

The First Sensing Circuit 142 may sense the combined sensingparameter(s) on the First Supply Rail 104, and the Second SensingCircuit 942 may sense the combined sensing parameter(s) on the SecondSupply Rail 904. By means of the First Sensing Circuit 142 and/or theSecond Sensing Circuit 942, the SLDAP Circuit 940 may perform thenecessary tasks to ascertain (i.e., detect or recognize) if an anomalyhas occurred in one or more of the n COTS in FIG. 9 , from COTS-1 130 toCOTS-n 132 in the COTS System 920. When an anomaly is detected, theSLDAP Circuit 940 may trigger the Control Line 144 to power cycle theSwitch 106, or the Control Line 944 to power cycle the Switch 906, orboth Control Lines 144 and 944 to power cycle both Switches 106 and 906.Conversely, where an anomaly is not detected, the Control Line 144 maykeep the Switch 106 closed, and the Control Line 944 may keep the Switch906 closed, thereby electrically coupling the First Power Rail 102 andthe First Supply Rail 104, and electrically coupling the Second PowerRail 902 and the Second Supply 104. The anomaly detection within SignalProcessing Circuit of the SLDAP Circuit 940 may leverage on the combinedsensing parameter on the First Power Rail 104 and/or the combinedsensing parameter on the Second Power Rail 904.

In the first variation of the second embodiment of the invention, theinvented SLDAP Apparatus 980 may improve its detection of an anomaly inthe COTS system 920 by correlating the combined sensing parameter(s) onthe First Power Rail 104 and the combined sensing parameter(s) on theSecond Power Rail 904.

In the second variation of the second embodiment of the invention, theinvented SLDAP Apparatus 980 may improve its detection of an anomaly inthe COTS System 920 by correlating the combined sensing parameter on theFirst Supply Rail 104 with the first embodiment (and its variousvariations) of the invention.

FIG. 10 depicts the third embodiment of the invention where theElectronics System 190 in FIG. 1 is now reconfigured Electronics System1090. In Electronics System 1090, the Output of the COTS System 1003 isinput to (or feedback) to the invented SLDAP Apparatus 180. In thisthird embodiment of the invention, the invented SLDAP 180 may recognizethat an anomaly has occurred in the COTS System 120 when the Output ofthe COTS system 1003 is unexpected, i.e., the output signal is unusualor unexpected. For example, consider that COTS System 120 that isexpected to output a time signal ranging from 00:00:00:00 to23:59:59:59. If the COTS System 120 however outputs a signal beyondthese limits, the invented SLDAP Apparatus 180 may assume that one ofthe COTS ICs in the COTS System 120 has suffered an anomaly. In anotherexample, if the same COTS system 120 unexpectedly outputs a repeatedtime signal, the invented SLDAP Apparatus 180 may similarly assume thatone of the COTS ICs in the COTS system has suffered an anomaly. In yetanother example, the Output of the COTS System 1003 is expected to pulsea signal every 10 ms. However, if it does not pulse every 10 ms, theoutput may be considered unexpected or unusual, i.e., an anomaly. Whenthe invented SDLAP Apparatus 180 detects the occurrence of an anomaly,it would thereafter power cycle the COTS System 120 by opening FirstSwitch 106 by means of First Control Line 144.

In the first variation of the third embodiment of the invention, theinvented SLDAP 180 may improve its detection of an anomaly in the COTSSystem 120 by correlating the Output of the COTS System 1003 with thefirst embodiment (and its various variations) of the invention.

In the second variation of the third embodiment of the invention, theinvented SLDAP 180 may improve its detection of an anomaly in the COTSSystem 120 by correlating the Output of the COTS System 1003 with thesecond embodiment (and its various variations) of the invention.

Although in most descriptions above, the current is the sensedparameter, note that both currents and voltages may be sensed. Further,variants of the current and/or voltage may also be sensed, for example,the differentiated or integrated versions thereto.

Also, most of the descriptions relate to current and/or voltage, and inmost cases referred to in the time-domain. Note that these signals maybe processed into the frequency-domain and processed by various signalprocessing algorithms.

While several embodiments have been provided in the present disclosure,it should be understood that the disclosed systems and methods may beembodied in many other specific forms without departing from the spiritor scope of the present disclosure. The present examples are to beconsidered as illustrative and not restrictive, and the intention is notto be limited to the details given herein. For example, the variouselements or components may be combined or integrated in another systemor certain features may be omitted or not implemented.

Also, techniques, systems, subsystems, and methods described andillustrated in the various embodiments as discrete or separate may becombined or integrated with other systems, modules, techniques, ormethods without departing from the scope of the present disclosure.Other items shown or discussed as directly coupled or communicating witheach other may be indirectly coupled or communicating through someinterface, device, or intermediate component, whether electrically,mechanically, or otherwise. Other examples of changes, substitutions,and alterations are ascertainable by one skilled in the art and could bemade without departing from the spirit and scope disclosed herein.

1. An apparatus for detecting an anomaly in an electronic systemcomprising: a power rail connected between a power source and a supplyrail, the supply rail connected to a distributed power rail, thedistributed power rail connected to a first sub-supply rail and to asecond sub-supply rail, a first electronic device and a secondelectronic device, the first sub-supply rail connected to a first pin ofthe first electronic device or to a first pin of the second electronicdevice or to the first pin of both the first and the second electronicdevices, the second sub-supply rail connected to a second pin of thefirst electronic device or to a second pin of the second electronicdevice or to the second pin of both the first and second electronicdevices, a signal processing system comprising a sensing circuit, thesensing circuit senses the characteristics of the current, the voltage,or both the current and voltage of the power rail or the supply rail anddetects the anomaly when the anomaly occurs in either the firstelectronic device or the second electronic device or both electronicdevices by sensing that the characteristics are different from that whenthe electronic system is functioning normally.
 2. The apparatusaccording to claim 1, wherein: the signal processing system furthercomprises a processing unit, the processing unit havingpre-characterized anomaly characteristics of at least one of the firstelectronic device, the second electronic device, or of any otherelectronic device, the pre-characterized anomaly characteristicsobtained by either measurement, estimation, modelling, machine-learning,or any combination of these, and when the anomaly occurs, the anomaly isfurther detected by the processing unit correlating thepre-characterized anomaly characteristics of at least one of the firstelectronic device, the second electronic device or the any otherelectronic device or a combination of the first electronic device andthe second electronic device and the any other electronic device withthe characteristics of the current, the voltage, or both the current andvoltage of the power rail or the supply rail sensed by the sensingcircuit.
 3. The apparatus according to claim 1, wherein the anomaly isdue to radiation effects or failure in either the first electronicdevice or the second electronic device, wherein the radiation effectsinclude single-event effects, including single-event-latchup,single-event-transient and single-event-upset, or total-ionizationdosage, or enhanced low dose rate effects, or neutron and protondisplacement damage, and wherein the failure includes malfunction, orcomputation errors.
 4. The apparatus according to claim 1, wherein whenthe anomaly is detected, the supply rail is disabled temporarily bydisconnecting the supply rail from the power rail for at least sometime, and thereafter reconnecting the supply rail with the power rail,or permanently by disconnecting the supply rail from the power rail. 5.The apparatus according to claim 4, wherein when the anomaly isdetected, the voltage of the supply rail is at least temporarily loweredor discharged to a pre-determined voltage.
 6. The apparatus according toclaim 4, wherein the disconnection or the connection or both arerealized by a switch placed between the power rail and the supply rail,and wherein the switch is either electronic or mechanical, or acombination of electronic and mechanical.
 7. The apparatus according toclaim 1, further comprising a power management circuit between thesupply rail and the distribution power rail, wherein the anomaly nowfurther arises from the power management circuit.
 8. The apparatusaccording to claim 2, wherein when an anomaly occurs, thepre-characterized anomaly characteristics of the first electronic deviceor of the second electronic device or of the any other electronic deviceare different from that the characteristics of the current, the voltage,or both the current and voltage sensed by the sensing circuit at thepower rail or the supply rail, and the pre-characterized anomalycharacteristics of the first electronic device, the second electronicdevice or the any other electronic device or that due to a combinationof electronic devices are correlated to or in some fashion resembling orrelated to the characteristics of the current, the voltage, or both thecurrent and voltage of sensed by the sensing circuit.
 9. The apparatusaccording to claim 2, wherein the processing unit has pre-characterizedanomaly characteristics of the power rail or supply rail, and whereinthe processing unit is analog, digital, or mixed-signal.
 10. Theapparatus according to claim 1, wherein the first electronic device andthe second electronic device are part of a larger electronic device, andwherein the larger electronic device is a complex integrated circuit, aSystem-on-Chip, a Field Programmable Gate Array, or a module embodyingseveral integrated circuits.
 11. The apparatus according to claim 1,wherein the electronic system further comprises: another power railconnected the power source and to another supply rail, the power railand the another power rail are of the same or different voltage, theanother supply rail connected to another distributed power rail, theanother distributed power rail connected to another first sub-supplyrail and to another second sub-supply rail, the another first sub-supplyrail connected to a third pin of the first electronic device, or to athird pin of the second electronic device, or to the third pin of boththe first and the second electronic devices, the another secondsub-supply rail connected to a fourth pin of the first electronic deviceor to a fourth pin of the second electronic device, or to the fourth pinof both the first and the second electronic devices, the signalprocessing system further comprising another sensing circuit, theanother sensing circuit senses the characteristics of either thecurrent, the voltage, or both the current or voltage of the anotherpower rail or the another supply rail, and when the anomaly occurs ineither the first electronic device or the second electronic device orboth the electronic devices, the anomaly is detected by either thesensing circuit or the another sensing circuit or both the sensingcircuit and the another sensing circuit sensing that the characteristicsof the another power rail or the another supply rail are different fromthat when the electronic system is functioning normally.
 12. Theapparatus according to claim 11, wherein when the anomaly occurs, theanomaly is further detected by the processing unit correlating any ofthe following: pre-characterized anomaly characteristics of either thefirst electronic device, the second electronic device or the any otherelectronic device or a combination of the first electronic device andthe second electronic device and the any other electronic device, thecharacteristics of the current, the voltage, or both the current andvoltage of the power rail or the supply rail sensed by the sensingcircuit, or the characteristics of the current, the voltage, or both thecurrent or voltage of the another power rail or the another supply railsensed by the another sensing circuit.
 13. The apparatus according toclaim 2, wherein the electronic system has an output, wherein the outputis input to the processing unit, and wherein the processor unit detectsan anomaly in the electronic system by detecting that signals in theoutput are unusual or unexpected.
 14. The apparatus according to claim2, wherein the processing unit has a correlation threshold value, andwherein the anomaly is detected when a correlation exceeds thecorrelation threshold value.
 15. The apparatus according to claim 14,wherein the processing unit employs statistical parameters of thecharacteristics of the current, the voltage, or both the current andvoltage of the power rail or the supply rail sensed by the sensingcircuit for its detection of the anomaly, and wherein the statisticalparameters include any one or a combination of the following parametersderived from the characteristics of the current, the voltage, or boththe current and voltage of the power rail or the supply rail sensed bythe sensing circuit: minimum, maximum, mean, variance, skewness,kurtosis, standard variation, median absolution deviation (MAD),k-statistic, and various rate of change of the characteristics of thesignal sensed.
 16. The apparatus according to claim 15, wherein thedetection of the anomaly is facilitated by a machine-learning algorithmby labelling, training and weighting the statistical parameters, andwherein the machine learning algorithm includes one or more or acombination of the following algorithms: decision tree, random forest,naïve bayes, support vector machine, K-nearest neighbours andconvolution neural network.
 17. The apparatus according to claim 16,wherein the correlation threshold value is based on probability as aresult of the machine learning algorithm, and wherein when thecorrelation threshold value exceeds 50%, an anormaly is assumed to haveoccurred.
 18. The apparatus according to claim 15, wherein an algorithmfor the detection of the anomaly is based on a correlation coefficient,and wherein the correlation threshold value is greater than two timesthe correlation coefficient in response to the occurrence of an anomalyover the non-occurrence of an anomaly.
 19. A method for detecting ananomaly in an electronic system comprising: sensing, by a sensingcircuit of a signal processing system in the electronic system,characteristics of the current, the voltage, or both the current orvoltage of a power rail of the electronic system or a supply rail of theelectronic system, wherein the power rail is connected a power sourceand to the supply rail, wherein the supply rail is connected to adistributed power rail, wherein the distributed power rail is connectedto a first sub-supply rail and to a second sub-supply rail, wherein thefirst sub-supply rail is connected to a first pin of a first electronicdevice or to a first pin of a second electronic device or to the firstpin of both the first and the second electronic devices, and wherein thesecond sub-supply rail is connected to a second pin of the firstelectronic device or to a second pin of the second electronic device orto the second pin of both the first and the second electronic devices;and when the anomaly occurs in either the first electronic device or thesecond electronic device or both the electronic devices, detecting theanomaly by the sensing circuit sensing that the characteristics aredifferent from that when the electronic system is functioning normally.20. The method according to claim 19, wherein: the signal processingsystem further comprises a processing unit, the processing unit havingpre-characterized anomaly characteristics of the first electronic deviceor of the second electronic device or of any other electronic device,the pre-characterized anomaly characteristics obtained by eithermeasurement, estimation, modelling, machine-learning, or any combinationof these, and when the anomaly occurs, the anomaly is further detectedby the processing unit correlating the pre-characterized anomalycharacteristics of the first electronic device, the second electronicdevice or the any other electronic device or a combination of the firstelectronic device and the second electronic device and the any otherelectronic device with the characteristics of the current, the voltage,or both the current and voltage of the power rail or the supply railsensed by the sensing circuit.